Knowledge base and mining for effective root-cause analysis

2. The system of claim 1, wherein the instructions further enable the processing device to classify the diagnostics by grouping the diagnostics into multiple groups based on a distance function related to the computed distances.

3. The system of claim 1, wherein the instructions further enable the processing device to utilize a weighting factor to determine normalized distances of the diagnostics, wherein the normalized distances are configured to account for different ranges of diagnostics from different network elements of the network.

4. The system of claim 1, wherein the instructions further enable the processing device to collect relevant information from the network elements and stream the relevant information to allow the symptoms to be derived.

5. The system of claim 4, wherein the relevant information includes one or more of Performance Monitoring (PM) data, performance metrics, errors, faults, issues, alarms, and network topology.

6. The system of claim 1, wherein the instructions further enable the processing device to conduct supervised ML to train an algorithm for recognizing the diagnostics.

7. The system of claim 1, wherein the instructions further enable the processing device to receive user feedback configured to revise one or more diagnostics stored in the database.

8. The system of claim 1, wherein the database is a Known error Data Base (KeDB).

9. The system of claim 1, wherein the instructions further enable the processing device to perform one or more remediation actions to correct or remediate the derived symptoms in the network.

10. The system of claim 1, wherein the network is a heterogeneous enterprise network.

12. The non-transitory computer-readable medium of claim 11, wherein the instructions further enable the one or more processing devices to classify the diagnostics by grouping the diagnostics based on a distance function related to the computed distances.

13. The non-transitory computer-readable medium of claim 11, wherein the instructions further enable the one or more processing devices to utilize a weighting factor to determine normalized distances of the diagnostics, and wherein the normalized distances are configured to account for different ranges of diagnostics from different network elements in the network.

19. The method of claim 15, wherein the database is a Known error Data Base (KeDB), and wherein the network is a heterogeneous enterprise network.

20. The method of claim 15, further comprising the step of performing one or more remediation actions to correct or remediate the derived symptoms in the network.